1. Field of the Invention
The present invention relates to an information processing apparatus, an information processing method, and a program. More specifically, the present invention relates to an information processing apparatus, an information processing method, and a program suitable for use in, for example, the personal identification of an individual that is the subject in a face image.
2. Description of the Related Art
Statistical learning methods have been used in the stage of learning in image recognition. Boosting, which is a learning technique based on statistical learning theory, can be used to construct high-accuracy classifiers capable of efficiently selecting feature values from voluminous data.
Classification of image recognition is generally formulated as a two-class separation problem of determining whether or not an image to be recognized matches a pre-learned image, and a great number of techniques for extending the classification to multi-class classification have been proposed.
For example, an application of Error-Correcting Output Coding (ECOC) used in the communication field, in which multiple classes are represented using binary numbers by repeating binary classification and are classified, is described in Thomas G. Dietterich and Ghulum Bakiri, “Solving Multiclass Learning Problems via Error-Correcting Output Codes”, Journal of Artificial Intelligence Research 2, pp. 263-286, 1995.
Further, for example, a mechanism in which the above ECOC method is applied to a boosting algorithm so that weak classifiers used for binary classification are combined to construct a multi-class classifier is described in Robert E. Shapire, “Using output codes to boost multiclass learning problems”, Proceedings of the Fourteenth International Conference on Machine Learning 1997.
Further, for example, it is described in T. Windeatt and G. Ardeshir, “Boosted ECOC Ensembles for Face Recognition”, International Conference on Visual Information Engineering, 2003 (VIE 2003), Volume, Issue, 7-9 Jul. 2003 pp. 165-168 that an extension of Adaptive Boosting (AdaBoost), which is a boosting algorithm, to a multi-class approach, i.e., Output-Code AdaBoost (AdaBoost.OC), is used for tasks for personal identification based on face images. Specifically, predetermined training images of 200 registered persons are used in the learning of a multi-class classifier. Face images of the same 200 persons are input to the multi-class classifier to evaluate to which class each face image belongs (that is, to which person out of the 200 registered persons each face image belongs).